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Style-guided text image generation tries to synthesize text image byimitating reference image's appearance while keeping text content unaltered.The text image appearance includes many aspects. In this paper, we focus ontransferring style image's background and foreground color patterns to thecontent image to generate photo-realistic text image. To achieve this goal, wepropose 1) a content-style cross attention based pixel sampling approach toroughly mimicking the style text image's background; 2) a pixel-wise stylemodulation technique to transfer varying color patterns of the style image tothe content image spatial-adaptively; 3) a cross attention based multi-scalestyle fusion approach to solving text foreground misalignment issue betweenstyle and content images; 4) an image patch shuffling strategy to create style,content and ground truth image tuples for training. Experimental results onChinese handwriting text image synthesis with SCUT-HCCDoc and CASIA-OLHWDBdatasets demonstrate that the proposed method can improve the quality ofsynthetic text images and make them more photo-realistic.

APRNet: Attention-based Pixel-wise Rendering Network for Photo-Realistic Text Image Generation